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Context-aware anomaly detection by community detection in the Internet of Things
No full textThis paper introduces a novel context-aware anomaly detection framework for the Internet of Things, leveraging community detection in multi-edge graphs with a heterogeneous Graph Neural Network (HeteroGNN) architecture to enhance network security. The proposed framework detects anomalies such as unexpected communication patterns among devices that rarely interact, unusual traffic spikes during off-hours, or deviations in the contextual and knowledge-based interactions of devices. For example, in an industrial IoT environment, unauthorized access or malicious activity can be inferred from unexpected communication within a device community after working hours. Our detection approach uses multi-edge graphs to model diverse interactions (network communication, context, knowledge) and applies community detection to capture stable graph structures. By incorporating these insights into a HeteroGNN, the framework effectively distinguishes anomalous edges while maintaining scalability and adaptability to dynamic network conditions. Experimental evaluation on the CIC-ToN-IoT and CIC-IDS2017 dataset demonstrates the framework’s superior accuracy, precision, and robustness, establishing it as a practical and effective solution for securing IoT networks against both known and emerging threats
Is There a Protective Effect of Exercise and Nutrition on Muscle Mass Reduction During Diet-Induced Weight Loss in Overweight and Obese Women? A Randomized Controlled 12-Week Trial
No full textPrediction of primary graft dysfunction and survival in heart transplantation using normothermic ex-vivo perfusion of the donor heart
No full textExercise for fracture prevention - evidence and consensus-based practice guideline of the osteology umbrella association (DVO)
No full textChemieprofessor Andreas Fath über den Schutz unserer Umwelt : "Der Einsatz jedes Einzelnen zählt"
No full textAI-Driven Tool Wear Prediction Under Severe Data Scarcity with SHAP-Guided Feature Selection and Fold-Safe Augmentation : A Case Study of Titanium Microdrilling
No full textParameter optimisation of a microreactor based continuous synthesis of bromooctyl glucoside by Fischer glycosylation
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